Methodological Advances Research Articles

Effects of non-isothermal aging and microalloying on precipitation strengthening for Al-Mg-Si alloys

The impacts of non-isothermal aging (NIA) and microalloying (Ag and/or Cu) on precipitation strengthening and mechanical properties in Al-Mg-Si alloys are investigated using advanced microscopic analysis techniques combined with tensile testing. The NIA treatment exhibits different age hardening for this alloy with different compositions. The Ag-Cu-added A3 alloy exhibits the most significant enhancement in peak hardness under NIA treatment than that of the isothermal aging (IA), while only marginal increases are observed for the other alloys (the base A1 alloy and the Cu/Ag-added alloys). The optimal NIA treatment for the Ag-Cu-added A3 alloy is identified as follows: 200 oC × 1h + cooling down to 180 oC at 20oC/h. This specific treatment leads to a peak hardness of 139.0 HV and an ultimate tensile strength of 384.0MPa for this alloy, which is 16.0 HV higher than that of the base Al-Mg-Si alloy when subjected to IA at 180oC. These findings provide valuable insights for the advancement of novel aluminum alloys and heat treatment methodologies.

Highly stable scalable production of porous graphene-polydopamine nanocomposites for drug molecule sensing

As atenolol overdosing can lead to severe health complications, the rapid detection of atenolol intake in point-of-care settings is highly desirable. The recent advancement of redox analytical methodologies has facilitated the efficacious quantification of these compounds for drug analysis, but their performance still presents challenges in practical applications. This study addresses these challenges by controlling the electropolymerization of polydopamine (PDA) on highly porous laser-induced graphene (LIG) electrodes with enhanced electrochemical redox activity for the detection of drug molecules such as atenolol, with minimized interference with the other active substances to induce variation of electrochemical behavior. The enhanced sensitivity of atenolol is attributed to the superhydrophilicity and increased number of active surface sites and -NH2 groups in the PDA polymer through a controlled polymerization process. Moreover, the simulation results further reveal that highly sensitive sensing of atenolol molecules relies on optimal adsorption of the atenolol molecule on dopamine or dopaminequinone structural units. The resulting sensors with high repeatability and reproducibility can achieve a low detection limit of 80 μM and a sensitivity of 0.020 ± 0.04 μA/μM within a linear range from 100 to 800 μM. The materials and surface chemistry in the electrode design based on highly porous LIG provide insights into the integration and application of future scalable and cost-effective electrochemical sensors for use in point-of-care or in-field applications.

Quantifying and Assessing Post-Stroke Patients' Functional Capability Level for Independent Daily Activities: A Review

Every year, over 13.7 million stroke episodes occur worldwide, addressing the pressing need for efficient rehabilitation treatments to restore motor function in stroke patients is paramount. The escalating number of stroke cases emphasises the urgency of developing robust rehabilitation methodologies. Currently, therapists rely on subjective and manual assessments to determine subsequent patient interventions, a method fraught with challenges, including subjectivity and dependence on the therapist's experience. This paper critically reviews the quantification and assessment of post-stroke patients' functional capability for independent daily activities. It delves into the existing standards of clinical assessment, explores the integration of sensors in limb rehabilitation prototypes, and observes the mathematical methods and algorithms employed by previous researchers to assess and quantify stroke patients' functional capability automatically. The discussion covers the advantages and disadvantages encountered in developing these prototypes and evaluating their suitability in addressing the complexities of stroke rehabilitation. In conclusion, this paper provides a perception of existing research and identifies crucial gaps. Based on the current research landscape, it presents proposals for prototype development, paving the way for future advancements in stroke rehabilitation methodologies.

Surgical management of traumatic dislocated lens with CM T Flex IOL implantation: A sutureless, glueless, T-haptic scleral fixated intraocular lens

Scleral fixated intraocular lenses (SFIOLs) have revolutionized aphakia management with compromised capsular support. Previous techniques, predominantly sutured SFIOLs, exhibited durability but were plagued by suture-related issues, necessitating advancements in fixation methodologies. In this case, a patient with lens dislocation post-ocular trauma underwent a vitrectomy with nucleus removal using the vacuum-assisted levitation method. Subsequently, the implantation of a novel CM T Flex IOL with a distinctive T-shaped haptic design was performed, obviating the need for haptic tucking or suturing. The surgical approach showcased heightened efficiency, minimized tissue handling, and a decreased risk of complications, collectively contributing to improved postoperative outcomes.

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension

Objective: Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a strategy that can improve the model’s capability to recognize biomedical entities in scenarios of few-shot learning.Methods: By redefining biomedical named entity recognition (BioNER) as a machine reading comprehension (MRC) problem, we propose a demonstration-based learning method to address few-shot BioNER, which involves constructing appropriate task demonstrations. In assessing our proposed method, we compared the proposed method with existing advanced methods using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical, BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA.Results: We examined the models’ efficacy by reporting F1 scores from both the 25-shot and 50-shot learning experiments. In 25-shot learning, we observed 1.1% improvements in the average F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%, 50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further improved the average F1 scores by 1.0% compared to the baseline method, reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively.Conclusion: We reported that in the realm of few-shot learning BioNER, MRC-based language models are much more proficient in recognizing biomedical entities compared to the sequence labeling approach. Furthermore, our MRC-language models can compete successfully with fully-supervised learning methodologies that rely heavily on the availability of abundant annotated data. These results highlight possible pathways for future advancements in few-shot BioNER methodologies.

Clinical glycoproteomics: methods and diseases.

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Revolution in malaria detection: unveiling current breakthroughs and tomorrow's possibilities in biomarker innovation.

The ongoing battle against malaria has seen significant advancements in diagnostic methodologies, particularly through the discovery and application of novel biomarkers. Traditional diagnostic techniques, such as microscopy and rapid diagnostic tests, have their limitations in terms of sensitivity, specificity, and the ability to detect low-level infections. Recent breakthroughs in biomarker research promise to overcome these challenges, providing more accurate, rapid, and non-invasive detection methods. These advancements are critical in enhancing early detection, guiding effective treatment, and ultimately reducing the global malaria burden. Innovative approaches in biomarker detection are leveraging cutting-edge technologies like next-generation sequencing, proteomics, and metabolomics. These techniques have led to the identification of new biomarkers that can be detected in blood, saliva, or urine, offering less invasive and more scalable options for widespread screening. For instance, the discovery of specific volatile organic compounds in the breath of infected individuals presents a revolutionary non-invasive diagnostic tool. Additionally, the integration of machine learning algorithms with biomarker data is enhancing the precision and predictive power of malaria diagnostics, making it possible to distinguish between different stages of infection and identify drug-resistant strains. Looking ahead, the future of malaria detection lies in the continued exploration of multi-biomarker panels and the development of portable, point-of-care diagnostic devices. The incorporation of smartphone-based technologies and wearable biosensors promises to bring real-time monitoring and remote diagnostics to even the most resource-limited settings.

TEMPORARY REMOVAL: Exploring the potential of reconstructed human epithelial tissue models for safety assessment of intraoral medical devices

Medical devices are integral to a wide array of medical interventions and are increasingly utilized in both clinical and home settings. Within the oral cavity, intraoral medical devices are employed for various applications, to improve quality of life and maintain oral health and hygiene. However, the dynamic and complex environment of the oral cavity, characterized by the influence of factors, such as saliva composition, fluctuating pH, and microbial flora presents a challenge to ensure the safety of end-users. In this paper, we investigate the feasibility of utilization of 3D reconstructed human tissue models for the assessment of biocompatibility of intraoral medical devices. Building upon experiences drawn from the development and validation of ISO 10993-23 and from the development of a protocol for ocular irritation and photo-irritation, we suggest a new protocol for buccal mucosa irritation testing. The methodology is based on the viability assessment and analysis of cytokine release into media. By addressing intraoral medical devices biocompatibility testing, we aim to contribute to the advancement of biocompatibility assessment methodologies and increase the applicability of ISO 10993-23.

Subsyndromes and symptom clusters: Multilevel factor analysis of behavioral and psychological symptoms of dementia with intensive longitudinal data.

Behavioral and psychological symptoms in dementia (BPSD) are dynamic phenomena with a high amount of intraindividual variability. We applied a multilevel framework to identify subsyndromes (between-person factors) that represent clinically relevant profiles of BPSD and identify symptom clusters (within-person factors) that represent contextually driven daily symptom experiences. This study used an intensive longitudinal design in which 68 co-residing family caregivers to persons living with dementia were recruited to proxy report on their care recipient's daily symptom experiences of 23 different BPSD for eight consecutive days (n=443 diaries). A multilevel exploratory/confirmatory factor analysis was used to account for nested data and separate within-person variances from between-level factor estimates. Exploratory factor analysis identified a 4-between 3-within factor structure based on fit statistics and clinical interpretability. This study offers major methodological and conceptual advancements for management of BPSD within Alzheimer's disease and related dementias by introducing two related but distinct concepts of subsyndromes and symptom clusters. Because behavioral and psychological symptoms of dementia (BPSD) are dynamic temporal phenomenon, this introduces measurement error into aggregate group-level estimates when trying to create subsyndromes. We propose a multilevel analysis to provide a more valid and reliable estimation by separating out variance due to within-person daily fluctuations. Using a multilevel exploratory factor analysis with intensive longitudinal data, we identified distinct and meaningful groups of BPSD. The four factors at the between-person level represented subsyndromes that are based on how BPSD co-occurred among persons with Alzheimer's disease (AD). These subsyndromes are clinically relevant because they share features of established clinical phenomena and may have similar neurobiological etiologies. We also found three within-person factors representing distinct symptom clusters. They are based on how BPSD clustered together on a given day for an individual with AD and related dementias. These clusters may have shared environmental triggers.

Kinetic investigation of the energy storage process in graphene fiber supercapacitors: Unraveling mechanisms, fabrications, property manipulation, and wearable applications

AbstractGraphene fiber supercapacitors (GFSCs) have garnered significant attention due to their exceptional features, including high power density, rapid charge/discharge rates, prolonged cycling durability, and versatile weaving capabilities. Nevertheless, inherent challenges in graphene fibers (GFs), particularly the restricted ion‐accessible specific surface area (SSA) and sluggish ion transport kinetics, hinder the achievement of optimal capacitance and rate performance. Despite existing reviews on GFSCs, a notable gap exists in thoroughly exploring the kinetics governing the energy storage process in GFSCs. This review aims to address this gap by thoroughly analyzing the energy storage mechanism, fabrication methodologies, property manipulation, and wearable applications of GFSCs. Through theoretical analysis of the energy storage process, specific parameters in advanced GF fabrication methodologies are carefully summarized, which can be used to modulate nano/micro‐structures, thereby enhancing energy storage kinetics. In particular, enhanced ion storage is realized by creating more ion‐accessible SSA and introducing extra‐capacitive components, while accelerated ion transport is achieved by shortening the transport channel length and improving the accessibility of electrolyte ions. Building on the established structure–property relationship, several critical strategies for constructing optimal surface and structure profiles of GF electrodes are summarized. Capitalizing on the exceptional flexibility and wearability of GFSCs, the review further underscores their potential as foundational elements for constructing multifunctional e‐textiles using conventional textile technologies. In conclusion, this review provides insights into current challenges and suggests potential research directions for GFSCs.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.

TEACHING INORGANIC CHEMISTRY TO MEDICAL UNIVERSITY STUDENTS: ENHANCING ENGAGEMENT THROUGH GAMIFICATION AND CONTEXTUALIZED LEARNING

Inorganic chemistry is fundamental to medical education, yet students often find it challenging and disconnected from clinical practice. This article explores innovative teaching strategies such as gamification, digital learning platforms, and contextualized instruction to enhance student engagement and comprehension. By integrating these methods with real-world applications, educators can make inorganic chemistry more relevant and accessible for medical students. The implications of incorporating advanced educational methodologies and digital resources are also discussed.

PROMOTING INNOVATIVE ACTIVITIES THROUGH ADVANCED MANAGEMENT AND FINTECH MODELS

Global economy is undergoing profound changes, impacting people's lives, businesses, and competitive landscapes. These shifts are driven by globalisation, continuous socio-economic development, the growing role of knowledge in the economy, and the intensified development and expansion of the European Union. In this evolving environment, new business models are becoming increasingly significant. Business and markets are no longer constrained by geographical boundaries. Products that were once confined to national markets are now exposed to international competition. With the removal of geographical restrictions, global markets for goods and services have essentially become operational around the clock. In response to these new challenges, companies adapt in different ways to new management and financial regulatory frameworks. To address the competitive challenges and increased competition, companies may choose to apply one or more business models that influence their overall performance. However, in the catering sector, particularly during the COVID-19 pandemic and quarantine periods, small and medium-sized enterprise managers have often neglected modern business management techniques, strategic planning, and company management. These elements are crucial for maintaining economic stability, forecasting future trends, addressing emerging issues, and safeguarding against factors that threaten business success and continuity. Recognising the increasing importance of strategic planning and management, alongside the need for scientific research and evidence-based recommendations, it has become essential to analyse a company's strategic position and its operational capabilities within this field. Every organisation must have a clear understanding of its current identity and future aspirations. For an organisation to grow and achieve its objectives successfully, it must employ advanced management methodologies and establish a well-defined strategy – a well-defined operational system. While setting goals can be straightforward, their realisation is significantly more complex. Once a company’s strategy is defined, it requires careful implementation. An action plan must be created to ensure the strategy's success, including budgeting, the development of an effective implementation mechanism, and tools for monitoring and evaluating the quality and progress of the plan. Employees responsible for specific strategy points and plan execution must be assigned. These are the next essential steps for the organisation. The key to success lies in responding swiftly to the ever-changing business environment and adjusting strategic directions and implementation measures as needed with a particular focus on strategic change management and the introduction of new, innovative management methodologies.

Scalability challenges of machine learning models for estimating walking and cycling volumes in large networks

This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.

Superhydrophobic FeNi3/Al2O3 multifunctional hybrid Janus particles for the catalytic degradation of azo dye, oil/water separation and microplastics removal

Emerging pollutants are causing global health and environmental challenges, necessitating the advancement of methodologies for their efficient removal. In this study, we present the efficacy of superhydrophobic FeNi3/Al2O3 Janus particles in the removal of oils and microplastics from water, combined with the degradation of azo dyes, leveraging their surface properties. Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray Spectroscopy (EDS) revealed the morphology and elemental composition of these particles, which were further complemented by X-ray diffraction (XRD) for phase identification. After surface functionalization of the FeNi3/Al2O3 Janus particles with lauric acid, they exhibited superhydrophobicity (152 ± 1°) and superoleophilicity (0°). High-resolution X-ray photoelectron spectroscopy (HR-XPS) showed that the alumina face was modified with lauric acid, while the intermetallic face remained as FeNi3. Significantly, the alumina face demonstrated 99 % efficiency in separating oils and microplastics, while the FeNi3 face facilitated the complete degradation (100 %) of methyl red within three minutes, as shown by ultraviolet-visible spectrophotometry. These results highlight the multifunctional properties of superhydrophobic FeNi3/Al2O3 Janus particles in removing and degrading various pollutants.

Due date-oriented picker routing, an efficient exact solution algorithm, and its application to pick-from-store omnichannel retailing

The advent of e-commerce and omnichannel retailing has sparked renewed interest in picker routing in warehouses. This paper presents two significant methodological advances in this well-established field. First, it is a well-known fact that the parallel-aisle layout of warehouses, as opposed to general graphs, allows for polynomial-time solutions of the Traveling Salesman Problem. We show that the parallel-aisle structure can also be exploited when pickers are tasked with visiting storage positions associated with specific due dates. We establish that picker routing in warehouses, subject to soft due date constraints, is a binary NP-hard problem. We also present an exact branch-and-bound algorithm with pseudo-polynomial time complexity. This algorithm effectively solves instances with up to 60 picking positions and five cross aisles within a few seconds while guaranteeing optimality. Second, for even larger pick lists, we demonstrate the successful integration of our algorithm into a real-time framework. This approach allows us to avoid extended solution times that would otherwise delay the picker’s departure, without compromising the quality of the solution. To illustrate the practical relevance of these two methodological innovations, we apply our routing algorithm to the context of pick-from-store omnichannel retailing. By assigning due dates to critical products, we significantly reduce stockout occurrences for online customers. These stockouts occur when the stock level, initially deemed sufficient to confirm an online order, is depleted by walk-in customers before the picker reaches the relevant shelf.

Enhancement of copper nanoparticle yield in magnetron sputter inert gas condensation by applying substrate bias voltage and its influence on thin film morphology

Large-scale synthesis of high-purity nanoparticles is an intense topic of scientific research, with magnetron sputter inert gas condensation recognized as a promising, environmentally friendly technique avoiding wet chemical processes. This study explores the deposition of size-selected nanoparticles under varied substrate bias voltages and reports on a consequent increase in deposition rates up to 32 %. These alterations in substrate bias voltage induce a progressive change in the morphology of the resulting nanoparticle thin films, attributable to the increased kinetic energy of the nanoparticles. Comprehensive characterization via quadrupole mass spectroscopy of the nanoparticle flux, scanning electron microscopy, X-ray photoelectron spectroscopy, and low-energy ion scattering spectroscopy of the deposited nanoparticles corroborates the enhanced nanoparticle yield associated with increased substrate bias voltage. These findings signify a methodological advancement, enhancing the efficiency of magnetron sputter inert gas condensation and moving the technology a step further towards feasible industrial production.

Leveraging metabolism for better outcomes in heart failure.

Whilst metabolic inflexibility and substrate constraint have been observed in heart failure for many years, their exact causal role remains controversial. In parallel, many of our fundamental assumptions about cardiac fuel use are now being challenged like never before. For example, the emergence of sodium glucose cotransporter 2 inhibitor (SGLT2i) therapy as one of the four "pillars" of heart failure therapy is causing a revisit of metabolism as a key mechanism and therapeutic target in heart failure. Improvements in the field of cardiac metabolomics will lead to a far more granular understanding of the mechanisms underpinning normal and abnormal human cardiac fuel use, an appreciation of drug action, and novel therapeutic strategies. Technological advances and expanding biorepositories offer exciting opportunities to elucidate the novel aspects of these metabolic mechanisms. Methodologic advances include comprehensive and accurate substrate quantitation such as metabolomics and stable-isotope fluxomics, improved access to arterio-venous blood samples across the heart to determine fuel consumption and energy conversion, high quality cardiac tissue biopsies, biochemical analytics, and informatics. Pairing these technologies with recent discoveries in epigenetic regulation, mitochondrial dynamics, and organ-microbiome metabolic crosstalk will garner critical mechanistic insights in heart failure. In this state-of-the-art review, we focus on new metabolic insights, with an eye on emerging metabolic strategies for heart failure. Our synthesis of the field will be valuable for a diverse audience with an interest in cardiac metabolism.

Parameter inference from a non-stationary unknown process.

Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze non-stationary processes, and yet, most time-series analysis methods that are used in practice on important problems across science and industry make the simplifying assumption of stationarity. One important problem in the analysis of non-stationary systems is the problem class that we refer to as parameter inference from a non-stationary unknown process (PINUP). Given an observed time series, this involves inferring the parameters that drive non-stationarity of the time series, without requiring knowledge or inference of a mathematical model of the underlying system. Here, we review and unify a diverse literature of algorithms for PINUP. We formulate the problem and categorize the various algorithmic contributions into those based on (1) dimension reduction, (2) statistical time-series features, (3) prediction error, (4) phase-space partitioning, (5) recurrence plots, and (6) Bayesian inference. This synthesis will allow researchers to identify gaps in the literature and will enable systematic comparisons of different methods. We also demonstrate that the most common systems that existing methods are tested on-notably, the non-stationary Lorenz process and logistic map-are surprisingly easy to perform well on using simple statistical features like windowed mean and variance, undermining the practice of using good performance on these systems as evidence of algorithmic performance. We then identify more challenging problems that many existing methods perform poorly on and which can be used to drive methodological advances in the field. Our results unify disjoint scientific contributions to analyzing the non-stationary systems and suggest new directions for progress on the PINUP problem and the broader study of non-stationary phenomena.